What is Fuzzing and How Does It Work?

Fuzzing is a testing method that sends random, malformed, or unexpected inputs to a program to identify potential vulnerabilities or crashes. It reveals bugs and security flaws by observing how the system handles unusual data. By analyzing the responses, fuzzing helps uncover issues that standard testing might miss. This approach strengthens the security and reliability of applications.

This blog explores key aspects of fuzzing, including its purpose, techniques, methodology, challenges, and best practices.

What is Fuzzing?

Fuzzing, a quality assurance method, identifies coding errors and security vulnerabilities in software, operating systems, and networks. It generates a large volume of random inputs to crash a system or provoke errors. When security teams and testers discover a vulnerability, they use a fuzz testing platform (or fuzzer) to determine the underlying cause. Fuzzing systems effectively uncover specific vulnerabilities, such as buffer overflows, denial of service (DoS) attacks, cross-site scripting, and code injection.

What is the Purpose of Fuzzing?

Fuzzing serves several critical purposes in software testing and security, enhancing the overall quality and resilience of applications.

Identifying Security Vulnerabilities

Fuzzing helps detect security vulnerabilities by sending unexpected or malformed inputs to a program. It uncovers issues like buffer overflows, injection flaws, or access control problems. This allows developers and application security engineers to address these vulnerabilities before attackers can exploit them.

Discovering Application Crashes

By bombarding a program with random inputs, fuzzing can cause crashes or exceptions that reveal weaknesses in the code. These crashes indicate areas where the application doesn’t handle unexpected data properly. Fixing these crash points improves the software's stability.

Testing Input Validation

Fuzzing tests how well an application validates and sanitizes input data. If the application accepts invalid or malicious inputs without proper validation, it becomes vulnerable to attacks. Fuzzing ensures that input validation routines are robust and secure.

Enhancing Test Coverage

Fuzzing expands test coverage by exploring input scenarios that security teams might not consider in traditional testing. It tests a wide range of inputs that go beyond normal use cases. This improves the overall quality of the software by catching more edge cases and potential issues.

Proactively Fixing Bugs

Fuzzing helps identify bugs early in the development process before they can lead to larger issues. By finding and fixing these problems early, developers and application security engineers can ensure that the software is more secure and reliable when it reaches production.

Types of Fuzzing Techniques

Fuzzing involves several distinct approaches, each tailored to specific testing scenarios and objectives. Let's explore the main types of fuzzing techniques:

1. Black-box Fuzzing

Black box fuzzing is a testing technique where no one knows the internal workings of the system being tested. Security teams and testers generate random or malformed inputs and feed them into the system to observe its behavior without understanding its code or logic. The goal is to uncover vulnerabilities, crashes, or unexpected behaviors based purely on external interactions.

An example of black box fuzzing is testing a web API without knowing its underlying code. Suppose a tester wants to fuzz the API endpoint /api/user?id=123. Without any knowledge of how the API processes the id parameter, the tester sends a variety of unexpected inputs, such as id=9999, id='admin', or id=@#$%. By observing the API’s responses, the tester can identify potential vulnerabilities, like SQL injection or improper access controls, without understanding the internal logic of the API.

2. White-box Fuzzing

White box fuzzing is a testing technique where the tester has full knowledge of the system’s internal structure, including its code, logic, and architecture. This insight allows for more targeted fuzzing by focusing on specific areas of the code that are more likely to contain vulnerabilities. Security teams and testers can generate inputs based on the code’s structure, ensuring deeper and more efficient testing.

For example, security teams and testers might focus on the function responsible for rendering HTML pages. By knowing the code, security teams and testers could create malformed HTML or CSS inputs that target edge cases or known vulnerabilities in rendering logic.

Because they understood how the function handled inputs internally, they could design more effective tests to trigger issues like memory corruption or buffer overflows. This targeted fuzzing helped Google improve the security and stability of Chromium by finding bugs that traditional testing methods might not have discovered.

3. Grey-box Fuzzing

Grey box fuzzing is a software testing technique where security teams and testers have partial knowledge of the system’s internal workings. It combines elements of both black box and white box fuzzing, allowing the security teams and tester to use some internal information—such as API documentation, code snippets, or architectural details—without having full access to the system’s source code.

For instance, in testing a mobile banking app, the security team might know the structure of API endpoints used for authentication and transaction processing, but they don’t have access to the full source code.

They can design fuzzing inputs based on this limited knowledge—like trying different types of malformed or unexpected authentication tokens or transaction amounts—to see how the system reacts. This approach allows them to focus on areas where vulnerabilities are likely while still testing the application from a near-user perspective.

4. Mutation-based Fuzzing

Mutation-based fuzzing is a technique where existing valid inputs are altered, or "mutated," to generate new test cases. Instead of creating inputs from scratch, mutation-based fuzzers take known good inputs and apply changes such as flipping bits, adding random characters, or modifying values to create variations. The goal is to see how the system handles these slightly altered inputs, potentially uncovering vulnerabilities like crashes, memory corruption, or security flaws.

For example, security teams and testers start with a set of valid JPEG image files that render correctly in the browser. The mutation-based fuzzer then applies small changes to these files—such as altering header data, flipping bits in the image encoding, or truncating the file—and submits the mutated images to the browser for rendering.

By observing how the browser handles these modified images, security teams and testers can identify issues like memory leaks, crashes, or even security vulnerabilities in the image decoding process. This method helped uncover several vulnerabilities in popular browsers by testing how they dealt with corrupted image files.

5. Generation-based Fuzzing

Generation-based fuzzing is a technique where test inputs are created from scratch based on a set of predefined rules or models. Unlike mutation-based fuzzing, which modifies existing inputs, generation-based fuzzing builds inputs according to the specifications or structure of the system being tested.

A real-world example of generation-based fuzzing is its use in testing network protocol implementations. In this case, a fuzzer generates network packets from scratch based on the protocol’s specification, such as TCP or HTTP.

The fuzzer creates various valid and invalid packet structures, including headers, payloads, and sequences, to simulate how different kinds of traffic would interact with the system. By sending these generated packets to a server, security teams and testers can identify vulnerabilities like buffer overflows or improper input handling that could lead to security breaches. Security teams and testers successfully used this approach to identify flaws in widely used network protocols, improving the security of network-based applications.

How Fuzzing Works?

Fuzzing employs a systematic approach to uncover vulnerabilities and bugs in software through a series of key steps.

Input Generation

The fuzzing process starts with input generation. Security teams and testers create a wide variety of inputs, ranging from completely random data to deliberately malformed values, to test the target application's limits.

Fuzzing tools generate inputs using methods like mutation-based approaches (modifying existing valid inputs) or generation-based methods (building inputs from scratch according to specific rules). This stage aims to ensure comprehensive coverage, testing the application's response to both expected and unexpected data. The diversity and quality of generated inputs critically affect fuzzing effectiveness. A wide input range helps identify edge cases, uncovering vulnerabilities that traditional testing might miss.

Execution

After input generation, security teams and testers feed these inputs into the application or system under test. During execution, the program processes the inputs while security teams and testers closely observe the system's behavior.

This step aims to see how the program handles unexpected or malformed data, whether it continues functioning as expected, or encounters errors or crashes. Security teams and testers may repeat the execution phase thousands or even millions of times, depending on the application's complexity and the fuzzing tool used. Each iteration sends a unique input into the system to observe the application's reaction. This phase tests the system's robustness and stability, pushing its boundaries and exposing how it deals with unusual situations.

Monitoring

Security teams and testers closely monitor the system during the execution phase. As the system processes each input, they observe its behavior to detect any anomalies. Effective monitoring involves tracking various system parameters, including crashes, memory leaks, abnormal outputs, and unexpected behaviors.

Security teams and testers often integrate monitoring tools with fuzzing tools to provide real-time feedback on system performance. For example, if the application crashes or consumes excessive memory, the monitoring system logs this behavior, indicating a potential vulnerability or bug.

Additionally, these tools can capture subtle signs of instability, such as slow performance or slight deviations from expected behavior, which may signal deeper issues requiring further investigation. The effectiveness of the fuzzing process largely depends on how well security teams and testers monitor the system, as undetected anomalies may lead to missed opportunities to identify serious flaws.

Logging

In the final step of the fuzzing process, security teams and testers log the test results. They record every anomaly or irregular behavior detected during the execution phase, creating a comprehensive log of potential issues. This logging serves multiple purposes.

First, it provides developers and application security engineers with a detailed record of what went wrong, which proves invaluable when diagnosing the root cause of the problem. Second, it helps prioritize which bugs or vulnerabilities to address first, based on the severity and frequency of the logged issues.

Logs typically include information such as the specific input that caused the failure, details about the failure itself (such as crash reports or memory dumps), and the system state at the time of the failure. Security teams and testers then analyze these logs to identify patterns or common points of failure, allowing developers to refine the code or system to prevent similar issues in the future. Effective logging forms the basis for post-fuzzing analysis and enables a more structured approach to fixing vulnerabilities.

Challenges of Fuzzing

While fuzzing powerfully tests software, it presents challenges that security teams and testers must address:

High Resource Consumption

Fuzzing consumes significant resources, especially when testing large applications or complex systems. It generates massive amounts of test data, overwhelming processing power, memory, and storage.

Tools like AFL or LibFuzzer often bind to the CPU, slowing down concurrent operations. Fuzzing can also take considerable time, particularly for unoptimized or comprehensive testing. As the input space grows, resource demands increase exponentially, necessitating effective hardware resource management or distributed fuzzing tests.

False Positives and False Negatives

Fuzzing inherently generates false positives, where it identifies non-issues as vulnerabilities, and false negatives, where it misses real vulnerabilities. Improper input generation or insufficient code coverage often cause these errors. False positives waste time on non-issues, while false negatives risk missing critical security flaws. While coverage-guided fuzzing reduces false negatives, manual review often remains necessary to minimize false positives and ensure effective testing.

Limited Input Scope

The quality of the input generation process heavily influences fuzzing effectiveness. Fuzzers that only generate random inputs without smart mutation strategies or coverage guidance may miss vulnerabilities, especially in edge cases or complex code paths. Inputs that don't sufficiently cover all possible code execution branches are more likely to overlook subtle bugs.

This limitation particularly affects black-box fuzzing, where limited system information reduces comprehensive testing potential. Expanding the input set or using generation-based fuzzers can mitigate this issue, but it remains a significant fuzzing challenge.

Complexity in Debugging

Fuzzing uncovers various vulnerabilities, including crashes, memory corruption, or undefined behavior. However, tracking down and fixing the exact cause of these issues often proves complex. This complexity increases when dealing with concurrency bugs, race conditions, or deep system-level issues that manifest sporadically.

Test cases causing crashes might not provide enough information to quickly isolate the problem, requiring developers to spend significant time debugging. Additionally, the randomness of inputs can sometimes lead to non-reproducible bugs, further complicating the debugging process.

Best Practices for Fuzzing

Effective fuzzing requires careful planning and execution. Follow these key best practices to maximize the fuzzing efforts:

Use Coverage-Guided Fuzzing

Implement coverage-guided fuzzing to target the right areas of the codebase efficiently. This method directs the fuzzer toward insufficiently tested code parts using feedback from code coverage tools. Prioritize untested or less-tested code sections to increase the likelihood of discovering new vulnerabilities. Tools like AFL (American Fuzzy Lop) and LibFuzzer effectively implement this technique by instrumenting the code and guiding input generation to maximize coverage.

Monitor Resources Efficiently

Actively monitor CPU, memory, and network usage during fuzzing tests to prevent system overload. Run fuzzing jobs in parallel on separate machines or leverage scalable cloud environments. Efficient monitoring ensures uninterrupted fuzzing processes and allows real-time resource allocation adjustments. Many fuzzing tools let security teams and testers set timeouts or resource limits to avoid system exhaustion, improving efficiency and reducing crash likelihood.

Start with Known Vulnerabilities

Focus on known vulnerabilities and use past bug patterns as a reference when starting the fuzzing efforts. This approach ensures security teams and testers test the most critical aspects of the application and validates the fuzzer's effectiveness.

Leverage known weaknesses to calibrate the fuzzer, prioritizing certain input types, mutation strategies, or code paths. This method proves especially useful when fuzzing mature applications that have have been significantly tested, ensuring regular revisits to critical areas.

Automate with CI/CD Integration

Integrate fuzzing into the Continuous Integration/Continuous Deployment (CI/CD) pipeline to catch vulnerabilities early in the development process. Run automated fuzzing tests continuously as part of the CI/CD cycle to detect new issues introduced by code changes.

This automation maintains security and quality without significant manual intervention. Implement this practice to maintain security over time, automatically testing each new build before it reaches production.

Prioritize Input Validation

Focus on areas of the code responsible for input validation to maximize the impact of the fuzzing efforts. These areas are particularly are particularly at risk to buffer overflows, injection attacks, and improper handling of user input. Strengthen input validation across the application to reduce the likelihood of exploited vulnerabilities. Use fuzzing to identify weak points in input validation routines, allowing security teams and testers to proactively reinforce them before malicious inputs reach the system.

Final Thoughts

Fuzzing plays a critical role in improving the security and robustness of software systems. By sending unexpected and malformed inputs, it helps uncover hidden vulnerabilities that regular testing methods may not uncover.

Automating fuzzing within development pipelines ensures continuous security checks as software evolves. This proactive approach helps developers and application security engineers address issues early, enhancing the overall stability and security of applications. Regular fuzzing is essential for building resilient systems in today's cybersecurity landscape.

Akto, an API security platform, offers powerful capabilities for performing API fuzzing. Akto can automatically test the APIs for various vulnerabilities, helping application security engineers catch security flaws and performance issues early. With Akto, security engineers can integrate fuzz testing seamlessly into the API security workflows. To see how Akto can help secure the APIs, book a demo today!